
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


lowerCamelCase_    :			Tuple    =	logging.get_logger(__name__)

@dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    str          =			field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()	)}	)
			__UpperCamelCase   :    str          =			field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}	)
			__UpperCamelCase   :    int          =			field(
			    default=128  ,							metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }  ,							)
			__UpperCamelCase   :    bool          =			field(
			    default=_A  ,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}	)

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.task_name.lower()




class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Dict          =			"""train"""
			__UpperCamelCase   :    List[Any]          =			"""dev"""
			__UpperCamelCase   :    Optional[int]          =			"""test"""




class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    GlueDataTrainingArguments
			__UpperCamelCase   :    str
			__UpperCamelCase   :    List[InputFeatures]

			def __init__(							self			:							int    ,						snake_case_			:							GlueDataTrainingArguments    ,						snake_case_			:							PreTrainedTokenizerBase    ,						snake_case_			:							Optional[int] = None    ,						snake_case_			:							Union[str, Split] = Split.train    ,						snake_case_			:							Optional[str] = None    ,						):
							warnings.warn(
							    """This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
							    """library. You can have a look at this example script for pointers: """
							    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""    ,						snake_case_    ,						)
							UpperCamelCase_:				Optional[int]						=    args
							UpperCamelCase_:				Optional[Any]						=    glue_processors[args.task_name]()
							UpperCamelCase_:				Dict						=    glue_output_modes[args.task_name]
							if isinstance(snake_case_    ,						snake_case_   ):
											try:
															UpperCamelCase_:				Optional[Any]						=    Split[mode]
											except KeyError:
															raise KeyError("""mode is not a valid split name"""   )
        # Load data features from cache or dataset file
							UpperCamelCase_:				Optional[int]						=    os.path.join(
							    cache_dir if cache_dir is not None else args.data_dir    ,						f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}'''    ,						)
							UpperCamelCase_:				List[str]						=    self.processor.get_labels()
							if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
							    "RobertaTokenizer",
							    "RobertaTokenizerFast",
							    "XLMRobertaTokenizer",
							    "BartTokenizer",
							    "BartTokenizerFast",
							):
											# HACK(label indices are swapped in RoBERTa pretrained model)
											UpperCamelCase_, UpperCamelCase_:				Dict						=    label_list[2], label_list[1]
							UpperCamelCase_:				Dict						=    label_list

							# Make sure only the first process in distributed training processes the dataset,
							# and the others will use the cache.
							UpperCamelCase_:				Tuple						=    cached_features_file + """.lock"""
							with FileLock(snake_case_   ):
											if os.path.exists(snake_case_   ) and not args.overwrite_cache:
															UpperCamelCase_:				Union[str, Any]						=    time.time()
															UpperCamelCase_:				Optional[int]						=    torch.load(snake_case_   )
															logger.info(
															    f'''Loading features from cached file {cached_features_file} [took %.3f s]'''    ,						time.time() - start   )
											else:
															logger.info(f'''Creating features from dataset file at {args.data_dir}'''   )

															if mode == Split.dev:
																			UpperCamelCase_:				Optional[Any]						=    self.processor.get_dev_examples(args.data_dir   )
															elif mode == Split.test:
																			UpperCamelCase_:				int						=    self.processor.get_test_examples(args.data_dir   )
															else:
																			UpperCamelCase_:				Optional[int]						=    self.processor.get_train_examples(args.data_dir   )
															if limit_length is not None:
																			UpperCamelCase_:				Any						=    examples[:limit_length]
															UpperCamelCase_:				Optional[int]						=    glue_convert_examples_to_features(
															    snake_case_    ,						snake_case_    ,						max_length=args.max_seq_length    ,						label_list=snake_case_    ,						output_mode=self.output_mode    ,						)
															UpperCamelCase_:				List[str]						=    time.time()
															torch.save(self.features    ,						snake_case_   )
															# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
															logger.info(
															    f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'''   )

			def __len__(							self			:							List[Any]   ):
							return len(self.features   )

			def __getitem__(							self			:							Dict    ,						snake_case_			:							List[str]   ):
							return self.features[i]




			def    lowerCAmelCase__						(							self			:							Any   ):
							return self.label_list








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCamelCase_    :			Optional[int]    =	{
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_    :			Union[str, Any]    =	[
							    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GraphormerForGraphClassification""",
							    """GraphormerModel""",
							    """GraphormerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_graphormer import (
														    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GraphormerForGraphClassification,
														    GraphormerModel,
														    GraphormerPreTrainedModel,
														)


else:
							import sys

							lowerCamelCase_    :			Optional[Any]    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






